Adaptive system for fan management

ABSTRACT

A system may include construction of observation vectors based on a plurality of power samples indicating past power consumption of a device and a plurality of temperature samples indicating past temperatures of the device, determination of an estimated temperature of the device based on fuzzy reasoning models comprising the observation vectors and a fan control parameter vector; measurement of a temperature of the device, determination of an error based on the estimated temperature and the measured temperature, and adaptation of the fan control parameter vector using a recursive least squares algorithm based on the error. The fans may be controlled based on the fan control parameter vector.

BACKGROUND

Conventional computing platforms use fan speed algorithms and stored parameters to control platform fans based on component temperatures. FIG. 1 illustrates a conventional Advanced Fan Speed Control architecture 100 for controlling fans based on a degree of thermal influence of each fan on a platform component or zone. Specifically, weighting matrix 110 provides influence coefficients indicating the relative influence of fans F1 and F2 on temperatures detected by temperature sensors T1 and T2. APWM boxes 120 and 130 generate a control action associated with each sensor using a PID (i.e., Proportional+Integral+Derivative) law. A PWM signal for each fan is then generated by multiplying the control action by a corresponding influence coefficient.

The parameters of weighting matrix 110 are determined and stored by a system integrator based on typical system configuration, usage and placement. The parameters are therefore not efficient for environmental conditions to either extreme of the typical conditions. The static set of parameters may therefore lead to thermal guard bands and suboptimal performance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system according to some embodiments.

FIG. 2 is a flow diagram of a process according to some embodiments.

FIG. 3 illustrates fuzzification of input variables according to some embodiments.

FIG. 4 is a block diagram of a system according to some embodiments.

DETAILED DESCRIPTION

FIG. 2 is a diagram of process 200 according to some embodiments. Process 200 may be executed by any combination of hardware, software and/or firmware, and some elements may be executed manually.

Initially, environmental variables are normalized at 210. The environmental variables may comprise any variables which may affect the temperature of an electronic device whose temperature is to be managed, including but not limited to fan speed, ambient temperature, and airflow velocity. In a specific example of 210, the environmental variables X₁ and X₂ represent the respective speeds of two platform fans. The normalized variables may therefore be represented as:

x ₁=(X ₁ −X _(1,min))/(X _(1,max) −X _(1,min)), and

x ₂=(X ₂ −X _(2,min))/(X _(2,max) −X _(2,min)).

The normalized variables are fuzzified at 220. Any system for fuzzification that is or becomes known may be used in some embodiments of 220. Some embodiments employ triangular, properly-overlapped membership functions for each input and Takagi-Sugeno fuzzy inference. FIG. 3 is a graph illustrating five triangular membership functions that may be used to fuzzify the normalized fan speed inputs of the present example.

Next, at 230, fuzzy reasoning models are created based on observation vectors and a fan control parameter vector. According to some embodiments, the normalized environment variables at instant k delayed by d samples (i.e., x₁(k−d) and x₂(k−d)) become the inputs to the fuzzy system.

The i-th fuzzy reasoning model may therefore become:

R _(i): IF x ₁(k−d)εX ₁ AND x ₂(k−d)εX ₂ THEN y _(i)(k)=A _(i)(q ⁻¹)y(k)+B _(i)(q ⁻¹)u(k),

where X₁ and X₂ are fuzzy sets corresponding to the aforementioned membership functions. y_(i)(k+1) is a one-instant-ahead value of junction temperature T_(j) of the device to be managed as predicted by the i-th model defined by A_(i)(q⁻¹), B_(i)(q⁻¹). The polynomials A(q⁻¹)=a₁q⁻¹+a₂q⁻²+ . . . +a_(n)q^(−n) and B(q⁻¹)=b₀+b₁q⁻¹+b₂q⁻²+ . . . +b_(m−1)q^(−m+1) represent a linear model, with q⁻¹ being a delay operator. y(k) is an observation vector of present and past values of the measured junction temperature T_(j), and u(k) is an observation vector of present and past values of the measured power consumption.

Hence, the consequent of the i-th rule can be rewritten as:

$\begin{matrix} {{y_{i}(k)} = {{{A_{i}\left( q^{- 1} \right)}{y(k)}} + {{B_{i}\left( q^{- 1} \right)}{u(k)}}}} \\ {= {{a_{1,i}{T_{j}\left( {k - 1} \right)}} + {a_{2,i}{T_{j}\left( {k - 2} \right)}} + \ldots + {a_{n,i}{T_{j}\left( {k - n} \right)}} +}} \\ {{{b_{0,i}{u(k)}} + {b_{1,i}{u\left( {k - 1} \right)}} + {b_{2,i}{u\left( {k - 2} \right)}} + \ldots + {b_{{m - 1},i}{{u\left( {k - m + 1} \right)}.}}}} \end{matrix}$

The foregoing elements of process 200 may be executed during design and/or testing of a platform in which the remaining elements of process 200 are to be executed. Accordingly, the platform itself might not execute any of the foregoing elements in some embodiments.

Determination of temperature y_(i)(k) for each of the i models requires the present and past values that comprise observation vectors y(k) and u(k). Accordingly, at 240, observation vectors are constructed using previously detected and stored samples indicative of device power consumption and device temperature. The samples indicative of device power consumption may comprise any signal correlated with active power. For example, the data samples include activity factors or signals derived from hardware performance counters.

Temperature sensors such as thermal diodes and/or digital thermometers may be employed to obtained the temperature samples used to construct observation vector y(k). In some embodiments of 240, the samples are obtained by superimposing a power trace that has rich frequency content in the range of interest (e.g., a pseudo-random binary sequence) on the operational load of the device.

An estimated temperature is determined based on the fuzzy reasoning models at 250. For example, an estimated temperature at instant k may be obtained by combining the estimates from the local models as follows:

y(k)=Σ_(i=1,N){(A _(i)(q ⁻¹)y(k)+B _(i)(q ⁻¹)u(k))w _(i)(x ₁ ,x ₂)},

where the weights w_(i) must satisfy the condition Σ_(i=1,N) w_(i)=1. The weights w_(i) represent the validity of each model based on the degree of membership of the fuzzy input variables.

Next, an error is determined based on the estimated temperature and on an actual measured temperature. The actual temperature may be measured at substantially instant k by any suitable system that is or becomes known. Generally, the error may be given by:

e(k)=T _(j)(k)−y(k).

At 270, it is determined whether the error is less than threshold value. If so, flow pauses at 280 before returning to 240 and continuing as described below. An error that is less than the threshold value therefore indicates that the fan control parameter vector does not require adaptation. Accordingly, the fan control parameter vector may be used during the pause at 280 to populate a weighing matrix of a fan control system such as system 100 of FIG. 1. The pause at 280 may be of any duration deemed suitable for retesting the suitability of the fan control parameter vector.

Flow proceeds to 290 if the error is greater that the threshold value. The fan control parameter vector is adapted at 290 using a recursive least squares parameter adaptation algorithm. Any suitable such algorithm may be employed at 290.

Continuing with the present example, the fan control parameter vector may be defined as θ=(a_(1,1) . . . a_(n,1)b_(1,1) . . . b_(m,1)a_(1,2) . . . a_(n,2) . . . b_(1,N) . . . b_(m,N)) by combining all the parameters of the fuzzy models and the information vector Φ(k)=(w₁T_(j)(k−1) . . . w₁T_(j)(k−n)w₁u(k) . . . w₁u(k−m−1)w₂T_(j)(k−1) . . . w₂T_(j)(k−n)w₂u(k) . . . w₂u(k−m−1) . . . w_(N)u(k−m−1)) in a similar way. The predicted temperature can therefore be expressed as y(k)=σ^(T)Φ(k), where T is the transpose operator.

The fan control parameter vector σ is then adapted at 290 using a least squares algorithm that provides stability and convergence. For example, using the Extended Least Squares algorithm, the adaptation algorithm corresponds to:

θ(k+1)=θ(k)+F(k)Φ(k)e(k+1),

F(k+1)=F(k)−F(k)Φ(k)Φ^(T)(k)F(k)/(1+Φ^(T) F(k)Φ(k)),

e(k+1)=(T(k+1)−θ^(T)(k)Φ(k))/(1+Φ^(T) F(k)Φ(k)).

FIG. 4 is a block diagram of a system to implement at least 240-290 of process 200 according to some embodiments. System 400 includes CPU 410, which may correspond to the device of interest in the previous example. CPU 410 is in communication with Memory Controller Hub 420 over a front side bus and with I/O Controller Hub 430 over a Platform Environment Control Interface. Thermal sensors 440 may measure temperatures as described above and provide the measured temperatures via Simple Serial Transport to management engine 425 of MCH 420.

MCH 420 also includes virtual thermal relationships table 427 for storing fan control parameters (i.e., a fan control parameter vector) according to some embodiments. Management engine 425 may execute any algorithm to generate control signals based on the stored parameters and to issue the control signals to ICH 430 via a Controller Link. ICH 430 may then control fans 450 based on the control signals.

Memory 460 is in communication with MCH 420 and may comprise, according to some embodiments, any type of memory for storing data, such as a Single Data Rate Random Access Memory (SDR-RAM), a Double Data Rate Random Access Memory (DDR-RAM), or a Programmable Read Only Memory (PROM).

The several embodiments described herein are solely for the purpose of illustration. Therefore, persons in the art will recognize from this description that other embodiments may be practiced with various modifications and alterations. 

1. A method comprising: constructing observation vectors based on a plurality of power samples indicating past power consumption of a device and a plurality of temperature samples indicating past temperatures of the device; determining an estimated temperature of the device based on fuzzy reasoning models comprising the observation vectors and a fan control parameter vector; measuring a temperature of the device; determining an error based on the estimated temperature and the measured temperature; and adapting the fan control parameter vector using a recursive least squares algorithm based on the error.
 2. A method according to claim 1, further comprising: fuzzifying a plurality of variables related to the device temperature, wherein the fuzzified plurality of variables comprise inputs to the fuzzy reasoning models.
 3. A method according to claim 2, further comprising: normalizing the plurality of variables prior to fuzzifying the plurality of variables.
 4. A method according to claim 1, wherein the plurality of variables comprise speeds of the fans.
 5. A method according to claim 4, wherein the plurality of variables comprise an ambient temperature.
 6. A method according to claim 1, further comprising: controlling the fans based on the fan control parameter vector.
 7. An apparatus comprising: a temperature device to measure a temperature of a device; a chipset to: construct observation vectors based on a plurality of power samples indicating past power consumption of the device and a plurality of temperature samples indicating past temperatures of the device measured by the temperature device; determine an estimated temperature of the device based on fuzzy reasoning models comprising the observation vectors and a fan control parameter vector; determine an error based on the estimated temperature and a temperature measured by the temperature device; and adapt the fan control parameter vector using a recursive least squares algorithm based on the error.
 8. An apparatus according to claim 7, the chipset further to: fuzzify a plurality of variables related to the device temperature, wherein the fuzzified plurality of variables comprise inputs to the fuzzy reasoning models.
 9. An apparatus according to claim 8, the chipset further to: normalize the plurality of variables prior to fuzzifying the plurality of variables.
 10. An apparatus according to claim 7, wherein the plurality of variables comprise speeds of the fans.
 11. An apparatus according to claim 10, wherein the plurality of variables comprise an ambient temperature.
 12. An apparatus according to claim 7, the chipset further to: control the fans based on the fan control parameter vector.
 13. A system comprising: a double data rate memory; a temperature device to measure a temperature of the memory; and a chipset to: construct observation vectors based on a plurality of power samples indicating past power consumption of the memory and a plurality of temperature samples indicating past temperatures of the memory measured by the temperature device; determine an estimated temperature of the memory based on fuzzy reasoning models comprising the observation vectors and a fan control parameter vector; determine an error based on the estimated temperature and a temperature measured by the temperature device; and adapt the fan control parameter vector using a recursive least squares algorithm based on the error.
 14. A system according to claim 13, the chipset further to: fuzzify a plurality of variables related to the device temperature, wherein the fuzzified plurality of variables comprise inputs to the fuzzy reasoning models.
 15. A system according to claim 14, the chipset further to: normalize the plurality of variables prior to fuzzifying the plurality of variables.
 16. A system according to claim 13, wherein the plurality of variables comprise speeds of the fans.
 17. A system according to claim 16, wherein the plurality of variables comprise an ambient temperature.
 18. A system according to claim 13, the chipset further to: control the fans based on the fan control parameter vector. 